DSS and banks

The term DSS may have different terms regarding different area. DSS has been widely and continually developing in tools and methods related to Online Analytical Procession (OLAP), data warehousing, data mining, model building, expert systems, neural networks, intelligent agents, group support systems, and communication capabilities for virtual teams. Nowadays, new terms have emerged such as BI and decision support applications.[1]

One example of a business that uses Decision Support Systems on a massive scale are banks and financial institutions. In the case of financial institutions, decision-making becomes even more critical, given large quantities of data and incomplete knowledge and understanding of available alternatives.[3] This is often directly tied into data from credit reporting agencies, who play a major role in not only banking but in any business that gives a consumer credit (like a Nissan dealership giving financing on a new car). Credit agencies use DSS that takes into consideration various financial data on a given consumer and produces a number that determines their credit worthiness. This is an example of a programmed decision[2], because all consumers are rated automatically in the same way based on the same criteria.

DSS gone wrong: During the recent housing bubble and the following mortgage crises, some banks decided to circumvent their DSS. What happened was some banks and bank employees decided to make decisions themselves, giving more money to people who should not have been eligible to receive it. This resulted in a ton of loans being giving out and the loan holders being unable to pay, combined with the falling housing market created a big problem. This has caused an outcry for banks to be more strict with their DSS and for there to be ramifications when they choose to ignore these systems.

In conclusion, Decision Support Systems are widely used in businesses. Banks are one example of a business that makes great use of DSS. Banks use DSS constantly to make critical financial decisions between them, their lenders and their customers. Banks are a great example of the value of DSS because of the number of decisions made and the far reaching financial implications of each of those decisions. Given that strategic decision making is central to a bank’s operations, the role of DSS naturally becomes crucial. With banking operations getting more complex and regulated, the use of DSS will only see an increase in the future. There is no other way banks can manage the vast data required to make timely and precise decisions, and to prevent unforeseen risks. If DSS is implemented properly in the banking sector, both effectiveness and efficiency of decision making will improve.[3] Without an advanced DSS banks would have little to no way to decide who to lend money to and how much they should lend, which would result in an extremely weakened if not nonexistent financial system.

 [1] http://www.diva-portal.org/smash/get/diva2:325280/FULLTEXT01.pdf

[2] Fundamentals of Information Systems textbook

[3] http://icreate.in/blog/business-intelligence/enabling-strategic-decision-making-the-role-of-decision-support-systems-in-banks